The identification of patient subpopulations most likely to respond to therapy is a central goal of modern molecular medicine. This notion is particularly important for cancer due to the large number of approved and experimental therapies (Rothenberg et al., 2003, Nat. Rev. Cancer 3:303-309), low response rates to many current treatments, and clinical importance of using the optimal therapy in the first treatment cycle (Dracopoli, 2005, Curr. Mol. Med. 5:103-110). In addition, the narrow therapeutic index and severe toxicity profiles associated with currently marketed cytotoxics results in a pressing need for accurate response prediction. Although recent studies have identified gene expression signatures associated with response to cytotoxic chemotherapies (Folgueria et al., 2005, Clin. Cancer Res. 11:7434-7443; Ayers et al., 2004, 22:2284-2293; Chang et al., 2003, Lancet 362:362-369; Rouzier et al., 2005, Proc. Natl. Acad. Sci. USA 102: 8315-8320), these examples (and others from the literature) remain unvalidated and have not yet had a major effect on clinical practice. In addition to technical issues, such as lack of a standard technology platform and difficulties surrounding the collection of clinical samples, the myriad of cellular processes affected by cytotoxic chemotherapies may hinder the identification of practical and robust gene expression predictors of response to these agents. One exception may be the recent finding by microarray that low mRNA expression of the microtubule-associate protein Tau is predictive of improved response to paclitaxel (Rouzier et al., supra).
To improve on the limitations of cytotoxic chemotherapies, current approaches to drug design in oncology are aimed at modulating specific cell signaling pathways important for tumor growth and survival (Hahn and Weinberg, 2002, Nat. Rev. Cancer 2:331-341; Hanahan and Weinberg, 2000, Cell 100:57-70; Trosko et al., 2004, Ann. N.Y. Acad. Sci. 1028:192-201). In cancer cells, these pathways become deregulated resulting in aberrant signaling, inhibition of apoptosis, increased metastasis, and increased cell proliferation (reviewed in Adjei and Hildalgo, 2005, J. Clin. Oncol. 23:5386-5403). Though normal cells integrate multiple signaling pathways for controlled growth and proliferation, tumors seem to be heavily reliant on activation of one or two pathways (“oncogene activation”). In addition to the well-known dependence of chronic myelogenous leukemia on BCR-ABL, studies of the epidermal growth factor receptor and MYC pathways showed that inactivation of a single critical oncogene can induce cell death or differentiation into cells with a normal phenotype (Lynch et al., 2004, N. Engl. J. Med. 350: 2129-2139; Paez et al., 2004, Science 304:1497-1500; Weinstein, 2002, Science 297:63-64; Jain et al., 2002, Science 297:102-104; Gorre et al., 2001, Science 293:876-880; Druker et al., 2001, N. Engl. J. Med. 344:1031-1037). The components of these aberrant signaling pathways represent attractive selective targets for new anticancer therapies. In addition, responder identification for target therapies may be more achievable than for cytotoxics, as it seems logical that patients with tumors that are “driven” by a particular pathway will respond to therapeutics targeting components of that pathway. Therefore, it is crucial that we develop methods to identify which pathways are active in which tumors and use this information to guide therapeutic decisions. One way to enable this is to identify gene expression profiles that are indicative of pathway activation status.
Current methods for assessing pathway activation in tumors involve the measurement of drug targets, known oncogenes, or known tumor suppressors. However, one pathway can be activated at multiple points, so it is not always feasible to assess pathway activation by evaluating known cancer-associated genes (Downard, 2006, Nature 439:274-275). To illustrate this situation, consider signaling through phosphatidylinositol 3-kinase (PI3K; FIG. 1). This pathway is activated by multiple growth factors through receptor tyrosine kinases and has effects on multiple processes, including cell growth and survival, metastatic competence, and therapy resistance. PI3K signaling is often activated in human cancers, and many pharmaceutical companies are developing inhibitors of one or more pathway components (Hennessy et al., 2005, Nat. Rev. Drug Discov. 4:988-1004). Therefore, accurate determination of PI3K pathway activation will be critical for the identification of potential responders to these emerging novel therapeutics.
However, the PI3K pathway can be activated by aberrations at multiple points, and assessing pathway activity may not be straightforward (Cully et al., 2006, Nat. Rev. Cancer 6: 184-192). For example, PI3K itself is frequently mutated in cancers. PI3K somatic missense mutations are common in HER2-amplified, hormone receptor-positive breast cancers, and PI3K mutation/amplification has been observed in ovarian cancer, gastric cancer, lung cancer, brain cancer, etc. (Bachman et al., 2004, Cancer Biol. Ther. 3:772-775; Samuels et al., 2004, Science 304:554; Campbell et al, 2004, Cancer Res. 64:7678-7681; Mizoguchi et al., 2004, Brain Pathol. 14:372-377; Shayesteh et al., 1999, Nat. Genet. 21:99-102; Woenckhaus et al., 2002, J. Pathol. 198:335-342). In addition, activating mutations in RAS occur in pancreatic and lung cancers (Johnson and Heymach, 2004, Clin. Cancer Res. 10:4254-4257), and a recent large-scale sequencing project in colorectal cancers recently identified novel infrequent mutations in PDK1 (Parsons et al., 2005, Nature 436:792). Finally, AKT (activation, amplification) and PTEN (mutation, deletion, epigenetic inactivation) are also deregulated in many human cancers (Altomare et al., 2003, J. Cell Biochem. 88:470-476; Ruggeri et al., 1998, Mol. Carcinog. 21:81-86; Cheng et al., 1996, Proc. Natl. Acad. Sci. USA 93:3636-3641; Staal et al., 1987, Proc. Natl. Acad. Sci. USA 84:5034-5037; Li et al., 2005, World J. Gastroenterol. 11:285-288; Li et al., 1997, Science 275:1943-1947; Goel et al., 2004, 64:3014-3021). Although PI3K pathway activation can be assessed by immunohistochemical analysis of PTEN or phosphorylated AKT levels in clinical samples (Slipicevic et al., 2005, Am. J. Clin. Pathol. 124:528-536), this may not be the optimal way to measure pathway activation. These assays are subject to the technical limitations of immunohistochemistry and are not quantitative. In addition, oncogenic pathways are complex (e.g., RAS signaling contributes to PI3K activation), so important pathway mediators may be missed by testing only a few well-characterized pathway components. The difficulty in measuring PI3K pathway activation by these means is reflected by inconsistent results reported in the literature when individual pathway components are analyzed in isolation (Saal et al., 2005, Cancer Res. 65:2554-2559; Panigrahi et al., 2004, J. Pathol. 204:93-100).
Examples like this suggest that a gene expression signature-based readout of pathway activation may be more appropriate than relying on a single indicator of pathway activity, as the same signature of gene expression may be elicited by activation of multiple components of the pathway. In addition, by integrating expression data from multiple genes, a quantitative assessment of pathway activity may be possible. In addition to using gene expression signatures for tumor classification by assessing pathway activation status, gene expression signatures for pathway activation may also be used as pharmacodynamic biomarkers, i.e. monitoring pathway inhibition in patient tumors or peripheral tissues post-treatment; as response prediction biomarkers, i.e. prospectively identifying patients harboring tumors that have high levels of a particular pathway activity before treating the patients with inhibitors targeting the pathway; and as early efficacy biomarkers, i.e. an early readout of efficacy.